CN115935120B - Goaf Scale Measurement and Calculation Method Based on Miaozi Remote Sensing Data - Google Patents
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Abstract
The invention discloses a goaf scale measurement calculation method based on muir remote sensing data, which adopts a genetic algorithm solving mode, divides a model of an initial form of a coal mine goaf into grids, respectively calculates adaptation values of individuals through a Monte Carlo program Geant4 in a mode of constructing a model population according to a density normal (1) and a density loss (0), generates a new population through multiple hybridization variation of the individuals, obtains a model solution set meeting conditions through a genetic algorithm, gives out a final model solution and model uncertainty on the basis of the model solution set through a bootstrap algorithm, and finally gives out a most reasonable three-dimensional density structure model and uncertainty of the coal mine goaf by performing inverse coding according to the density normal (1) and the density loss (0). The invention overcomes the limitation of the traditional technology in complex goaf detection, and can give out a three-dimensional density structural model of the goaf of the coal mine and corresponding uncertainty.
Description
Technical Field
The invention belongs to the field of geological exploration treatment, and relates to a detection scale reconstruction method of a coal mine goaf based on muon remote sensing data.
Background
Coal is the most important one-time energy source in China for a long time, and the construction of environment-friendly, benefit-type and safety-type coal mines is the necessary premise of coal mine production, and the problems of safety, environment protection, coal mine benefit and the like are very important to the control of mine geological disasters.
The goaf of the coal mine is a common mine disaster, the old roof at the rear of the coal face collapses in the range of the falling step pitch, the old roof collapses under the action of mine pressure, the goaf presents dynamic continuity change, a large number of gaps, large holes and other void spaces with different scales are generated, and therefore, the grasping of a complex goaf geometric structure is extremely necessary.
In the past, when a goaf flow field is processed, the goaf is assumed to be a continuous medium, and is regarded as a porous medium category based on an REV concept. The method is influenced by various factors such as physical and mechanical properties of the coal and rock, joint, coal mining method, mining height and the like, the shapes of the coal and rock fragments are various, the formed cavity space is changed in a huge variety, and the cavity morphology of a newly-produced goaf of the same stope face is dynamically and randomly changed. It can be said that two identical new goaf void morphologies cannot be found either in time or in space. In addition, due to limitations of test conditions and equipment, no reasonable test method is found at present to know the real internal cavity structure of the goaf.
In the prior art, the goaf range and the residual settlement are evaluated and judged usually through drilling and geophysical prospecting, the method is only suitable for determining goafs in small ranges, and drilling and geophysical prospecting objects are blind and have no pertinence; specifically, the drilling means is to arrange 1 supplementary exploration line along goaf trend, and supplementary exploration drilling holes are constructed on the supplementary exploration line at intervals, and the drilling holes cannot cover the survey line and the region outside the survey line in a high density manner, and may cause goaf collapse.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a goaf scale measurement calculation method based on muon remote sensing data, which is characterized in that a cosmic ray receiving device is utilized to detect the change of a cosmic ray when the cosmic ray passes through holes with different density lengths, and a three-dimensional density inversion algorithm is adopted to detect the density abnormality of the goaf, so that the form of the internal density structure of the goaf is given.
In order to solve the technical problems, the invention adopts the following technical scheme:
a goaf scale measurement calculation method based on muon remote sensing data comprises the following steps:
step 2, based on the ratio observed in step 1 Experiment Reconstructing a goaf model, arranging the positions of muon detectors in a step 1, designating the accuracy dimensions grid_x, grid_y and grid_z of the reconstructed goaf model according to the size of an observation area, dividing the observation area into three-dimensional grids which are totally Nx multiplied by Ny multiplied by Nz grid solving areas to form a coal mine goaf three-dimensional grid lattice; then, randomly setting the grid of the three-dimensional grid lattice of the goaf of the coal mine, which is filled by the coal rock mass, as 1, and not being filledThe grid of the goaf is set to 0, and the initial model form of the coal mine goaf is randomly generated;
step 3, model population gene coding and goaf model population t generation: the goaf model constructed according to the step 2 has Nx Ny x Nz grid density parameters which are respectively replaced by 1 and 0, and the gene length is Nx Ny x Nz; generating a population t with N individuals by randomly changing the arrangement sequence of 0 and 1, wherein the population is the goaf initial morphology model population;
step 4, creating Nx, ny and Nz geometric models with the size of grid_x, grid_y, grid_z in the Geant4 simulation program, performing density assignment according to the population genes in the step 3 to form a goaf physical model, and setting a muon detector at the same observation position as the step 1; using CRY cosmic ray generation sub-model cosmic ray source to calculate muoncounts (theta, phi) received by muon detectors arranged at observation positions under goaf model to be measured, and then calculating ratio corresponding to each observation position Model (θ，φ)；
Step 5: population adaptation value calculation: repeating the step 4 until the calculation of N model goaf individuals in the population is completed, and obtaining the histogram parameter ratio of N groups of model residual flux distribution Model (θ, φ); the adaptation value alpha is defined as the histogram parameter ratio of the model residual flux distribution Model Histogram parameter ratio of (θ, φ) to experimental residual flux distribution Experiment Variance of (θ, φ):
wherein i and j are the number of angle interval divisions of zenith angle theta and azimuth angle phi respectively;
step 6, excellent population individual hybridization variation: the adaptation values of N individuals in the step 5 are sorted from small to large, 2N/3 individuals are removed, the density chromosomes of the rest N/3 individuals are subjected to hybridization variation to generate new 2N/3 individuals and the original winning N/3 individuals to form a new generation population t+1 of N individuals;
step 7, repeating the steps 4-6, outputting the density genes of N/3 partial population individuals when the adaptation values of N/3 individuals in the population are smaller than the designed relative error threshold, and decoding the density genes as a coal mine goaf three-dimensional density structure model meeting the conditions;
and 8, the N/3 three-dimensional density structure models obtained in the step 7 are not unique, so that the most reasonable coal mine goaf three-dimensional density structure model and corresponding uncertainty are obtained by continuously adopting a bootstrap resampling method.
The invention also comprises the following technical characteristics:
specifically, in the step 1, the coal mine goaf model is a natural coal mine goaf or a manually placed square block with different densities.
Specifically, the muon detector is a plastic scintillator array detector, a gas detector or a nuclear latex detector.
Specifically, in the step 1, the zenith angle θ and the azimuth angle Φ:
；
the method comprises the steps of carrying out a first treatment on the surface of the Wherein x=x in -x out ;y=y in -y out ;z=z in -z out 。
Specifically, in the step 2, the typical density of the grid corresponding to the coal rock mass 1 is 1.4g/cm 3 A typical density of the grid of 0 corresponding to the goaf is 0.5g/cm 3 。
Specifically, in the step 8, the bootstrap resampling method includes:
the first step is that K resampling is carried out on N/3 population data to form a plurality of bootstrap data sets, each resampling is random sampling, and the number of samples is the same as the number of the population data set elements;
the second step is that on the basis of an inversion frame, K bootstrap data sets are utilized to carry out K times of Geant4 modeling calculation to obtain an inversion result combination consisting of K density models, and the initial model, relevant inversion parameters and iteration times required by each calculation are the same;
and thirdly, carrying out statistical analysis and evaluation on the calculation result combination, indicating the uncertainty of the model according to the difference condition among the bootstrap calculation models, and finally giving out the most reasonable coal mine goaf three-dimensional density structure model and the corresponding uncertainty.
Compared with the prior art, the invention has the following technical effects:
according to the invention, a model most conforming to experimental observation data is searched by utilizing a principle of sensitivity of cosmic ray muon to opacity (density length) of a substance and utilizing a Meng Daka-roar simulation mode through a genetic algorithm; the method overcomes the limitation of the traditional technology in complex goaf detection, and can give out a three-dimensional density structural model of the goaf of the coal mine and corresponding uncertainty.
Drawings
FIG. 1 is a schematic model of an artificially constructed simulated goaf;
FIG. 2 is a side view of a coal mine goaf model;
FIG. 3 is a randomly generated goaf model;
FIG. 4 is a density inversion model obtained by decoding calculation.
Detailed Description
The invention provides a goaf scale measurement calculation method based on muir remote sensing data, which is a reconstruction method of the shape and position of a coal mine goaf, adopts a genetic algorithm solving mode, divides a model of the initial shape of the coal mine goaf into grids, carries out coding according to a density normal (1) and a density loss (0) to construct a model population, respectively calculates the adaptation value of an individual through a Monte Carlo program Geant4, generates a new population through multiple hybridization variation of the individual, obtains a model solution set meeting the condition through the genetic algorithm, gives out a final model solution and model uncertainty on the basis of the model solution set through the bootstrap algorithm, and finally carries out inverse coding according to the density normal (1) and the density loss (0) to give out the most reasonable three-dimensional density structure model and uncertainty of the coal mine goaf. The method overcomes the limitation of the traditional technology in complex goaf detection, and can give out a three-dimensional density structural model of the goaf of the coal mine and corresponding uncertainty.
The following specific embodiments of the present invention are provided, and it should be noted that the present invention is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solutions of the present application fall within the protection scope of the present invention.
Example 1:
the embodiment provides a goaf scale measurement calculation method based on muon remote sensing data, which comprises the following steps:
in the step 1, a coal mine goaf model is a natural coal mine goaf or a manually placed square block with different densities; the muon detector is a plastic scintillator array detector, a gas detector or a nuclear latex detector; the zenith angle theta ranges from 0 to 90 degrees, and the angle interval can be divided into 18 angle intervals according to 5-degree division; the azimuth angle phi ranges from 0 to 360 degrees, and the angle interval can be divided into 10 angle intervals according to 36-degree division, so that the whole detection space is divided into 18 x 10 = 180 angle intervals;
specifically, in this embodiment, observation data of the muon is first obtained, and the data may be obtained according to actual observation or may be obtained through analog calculation; the embodiment describes a data acquisition mode in the actual observation process, wherein the used equipment is a muon detector with position resolution, and the mode of acquiring data by muon equipment data is to record the concentrated incident position coordinates (x in ,y in ,z in ) Emission position coordinates (x out ,y out ,z out ) The determination of the position coordinates of the muon can be obtained through detection by various detectors (such as a plastic scintillator array detector, a gas detector, a nuclear latex detector and the like), and although the data acquisition mode is not unique, the finally-given data mainly comprises the position information of the muon; the method protects the data processing process after the position of the cosmic ray muon is acquired;
the experimental data can be obtained by carrying out experiments in natural coal mine goaf, or by manually setting squares with different densities to construct a coal mine goaf model for experiments; when the test starts, a cosmic ray position sensitive monitoring sensor is respectively arranged below the natural goaf model or the artificial goaf model; record the incidence of the received cosmic ray (x in ,y in ,z in ) And the exit position (x out ,y out ,z out ) The method comprises the steps of carrying out a first treatment on the surface of the Fig. 1 shows a schematic model of a simulated goaf constructed manually, fig. 2 shows a side view of a goaf model of a coal mine, wherein a gray area is a bedrock without abnormality, a white area is a goaf, and p1-p5 are 5 observation positions respectively.
The muon monitoring sensors at 5 observation positions record the incidence of the received cosmic rays respectivelyx in ,y in ,z in ) And the exit position (x out ,y out ,z out ) And respectively calculating the zenith angle theta and the azimuth angle phi of the track:
wherein x=x in -x out ;y=y in -y out ;z=z in -z out ；
Then dividing different intervals according to the angle range, respectively counting the number of muon in each angle interval (for example, the theta range is 0-90 degrees, the angle interval range can be divided into 18 intervals according to the 5-degree division, the phi range is 0-360 degrees, the angle interval range can be divided into 10 intervals according to the 36-degree division, then the whole detection space is divided into 18 x 10 = 180 angle intervals), and obtaining a distribution histogram parameter muoncounts (theta, phi) of the flux of the cosmic ray at each position under the model to be detected along with the direction. The histogram parameter muonopsesky (theta, phi) of the distribution of the cosmic ray flux of the open sky above the model to be measured along with the direction can be measured in the same way, and the residual flux ratio of the cosmic ray is further calculated: ratio Experiment (θ, φ) =muoncounts (θ, φ)/muonopsensky (θ, φ), a histogram parameter ratio of the cosmic ray residual flux distribution Experiment (θ, φ) is used to characterize the opacity parameter across the physical similarity model in various directions at the detector location.
Step 2, reconstructing a goaf model based on histogram parameters (theta, phi) of the residual flux distribution of the cosmic ray observed in the step 1, arranging the positions of muon detectors in a mode of the step 1, designating grid precision dimensions grid_x, grid_y and grid_z of the reconstructed goaf model according to the size of an observation area, dividing the observation area into three-dimensional grids, and forming a coal mine goaf three-dimensional grid lattice by using Nx Ny Nz grid solving areas; then, randomly lattice the three-dimensional grid of the goaf of the coal mineThe grid filled by the coal rock mass is set as 1, the grid of the goaf not filled is set as 0, the initial model form of the goaf of the coal mine is randomly generated, and the typical density of the grid of the 1 corresponding to the coal rock mass is 1.4g/cm 3 0 corresponds to an unfilled grid typical density of 0.5g/cm 3 ；
As shown in fig. 3, the positions of the muon detectors P are arranged in the same manner as the test, P1-P5 are respectively 5 observation positions, and according to the reconstructed grid precision dimensions grid_x, grid_y and grid_z specified by the sizes of the observation regions, the three-dimensional grid is divided into a three-dimensional grid solving region (taking the initial form physical model grid of the coal mine goaf of fig. 3 as nx=27, ny=1 and nz=9 as an example) to form a three-dimensional grid lattice of the coal mine goaf. Then, randomly setting the grid of the three-dimensional grid lattice of the coal mine goaf filled by the coal rock mass as 1, and setting the unfilled grid of the unfilled goaf as 0; model initial morphology of randomly generated coal mine goaf (a model randomly generated is shown in FIG. 3), typical density of 1 corresponds to 1.4g/cm 3 0 corresponds to an unfilled grid typical density of 0.5g/cm 3 。
Step 3, model population gene coding and goaf model population t generation: the goaf model constructed according to the step 2 has Nx Ny x Nz grid density parameters which are respectively replaced by 1 and 0, and the gene length is Nx Ny x Nz; generating a population t with N individuals by randomly changing the arrangement sequence of 0 and 1, wherein the population is the goaf initial morphology model population; for example, fig. 2 shows an individual in a randomly generated population t.
Population gene coding: the biological DNA gene carries genetic information of organisms, but the coding units of the biological DNA gene are only 4 (adenine deoxyribonucleotide, guanine deoxyribonucleotide, cytosine deoxyribonucleotide and thymine deoxyribonucleotide), and various genetic information can be formed through different coded permutation and combination, and finally the biological DNA gene is expressed as diversity of the organisms. The code of the invention is similar to the above, and is a mode of corresponding density value by digital code, the typical density of the code 1 corresponding to the coal rock mass in the scheme is 1.4g ∈ -cm 3 The typical density of the grid filled with the goaf corresponding to the code 0 is 0.5g/cm 3 。
Goaf model population: after the coding mode is determined, different sequences with the length of Nx Ny Nz are generated through different sequences of 0 and 1 in a mode of step 2, coding information carried by one sequence represents a goaf density structure model, and the goaf density structure model is equivalent to an individual in the set of goaf density structures; the collection of these goaf model individuals is referred to as a goaf model population.
Step 4, creating Nx, ny and Nz geometric models with the size of grid_x, grid_y, grid_z in the Geant4 simulation program, performing density assignment according to the population genes in the step 3 to form a goaf physical model, and setting a muon detector at the same observation position as the step 1; using CRY cosmic ray generation sub-model cosmic ray sources to calculate muoncounts (theta, phi) received by muon detectors arranged at observation positions under goaf models to be detected, and then calculating ratio models (theta, phi) corresponding to each observation position;
specifically, in this embodiment, nx×ny×nz geometric models with dimensions of grid_x×grid_y×grid_z are created in the Geant4 simulation program, and density assignment is performed to form a goaf physical model according to "density genes" in step 3 (genes in biology are nucleotide sequences carrying genetic information; the expression mode of biology is referred to in the invention by reference to biology, to describe a length nx×ny×nz coding sequence generated by goaf model individuals according to the coding rules, because the coding values correspond to density information, they are called density genes), and a cosmic line detector is set at the same p1-p5 position as in step 1. Using CRY cosmic ray generation sub-model cosmic ray source to calculate the muoncounts (theta, phi) received by the cosmic ray detector set at the p1-p5 position under the goaf model to be measured, and then respectively calculating the corresponding ratio Model (θ，φ)。
Step 5, population adaptation value calculation: repeating the step 4 until the calculation of N model goaf individuals in the population is completed, and obtaining the orthometric of N groups of model residual flux distributionA graph parameter ratio model (θ, φ); the adaptation value alpha is defined as the histogram parameter ratio of the model residual flux distribution Model Histogram parameter ratio of (θ, φ) to experimental residual flux distribution Experiment Variance of (θ, φ):
in the above expression, i denotes the number of bins of zenith angle θ, and j denotes the number of bins of azimuth angle Φ.
Step 6, excellent population individual hybridization variation: the adaptation values of N individuals in the step 5 are sorted from small to large, 2N/3 individuals are removed, the density chromosomes of the rest N/3 individuals are subjected to hybridization variation to generate new 2N/3 individuals and the original winning N/3 individuals to form a new generation population t+1 of N individuals;
step 7, repeating the steps 4-6, outputting the density genes of N/3 partial population individuals when the adaptation values of N/3 individuals in the population are smaller than the designed relative error threshold, and decoding the density genes as a coal mine goaf three-dimensional density structure model meeting the conditions;
and 8, the N/3 three-dimensional density structure models obtained in the step 7 are not unique, so that a bootstrap resampling method is continuously adopted to obtain the most reasonable coal mine goaf three-dimensional density structure model and corresponding uncertainty, and a density inversion model obtained through decoding calculation is shown in fig. 4. Specifically, the bootstrap resampling method comprises the following steps: the first step is that K resampling is carried out on N/3 population data to form a plurality of bootstrap data sets, each resampling is random sampling, and the number of samples is the same as the number of the population data set elements; the second step is that on the basis of an inversion frame, K bootstrap data sets are utilized to carry out K times of Geant4 modeling calculation to obtain an inversion result combination consisting of K density models, and the initial model, relevant inversion parameters and iteration times required by each calculation are the same; and thirdly, carrying out statistical analysis and evaluation on the calculation result combination, indicating the uncertainty of the model according to the difference condition among the bootstrap calculation models, and finally giving out the most reasonable coal mine goaf three-dimensional density structure model and the corresponding uncertainty.
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